AI Autos: Leave the Driving to Us

AI Revolution

The 200-mile trip from San Francisco to Lake Tahoe can be a frustrating slog in the wintertime traffic on Interstate 80. Speeds in the fast lane swing from 90 to 30 for no discernible reason. Slow, fast, faster, slow. Hit rush hour in Sacramento—or Donner Pass on a snowy day—and you’ll see the speedometer’s needle tapping the 10 mph mark like a woodpecker on a tasty log.

Stick-shift drivers collapse with dead legs on the side of the road; even the P-R-N-D crowd can be seen massaging their sore knees at roadside burger joints and woodsy rest stops.

Not me. I’m playing the license plate game and humming through playlists with a few friends, happy and comfortable in a borrowed Mercedes-Benz S550, a luxury sedan that’s currently justifying the pants off its $100,000 window sticker. We’re bopping through the same unpredictable range of velocities as everyone else, but I haven’t touched a pedal in hours.

Fraud Detection

The neural nets are watching.

Credit card fraud costs US merchants and credit card companies more than $3.4 billion a year. That figure would undoubtedly be much higher without the use of computer surveillance systems to monitor every transaction.

One of the most proven antifraud systems is FICO’s Falcon Fraud Manager, which keeps tabs on more than 4 billion transactions a month and uses lightning-fast neural networks to scan for suspicious purchase patterns. Neural networks were originally designed to mimic human gray matter. Over time, however, the technology has moved far beyond brain simulation to become a basic building block of many computer systems capable of learning and pattern recognition. The networks typically consist of layers of interconnected “neurons,” each of which produces a signal only when its input exceeds a certain threshold. Though the individual neurons are simple, the net as a whole can learn to recognize complex patterns of inputs.

The Falcon system specializes in detecting things a human would never notice. For example, if you use your card to buy a tank of gas and then go directly to a jewelry store to make a purchase, your account will almost surely be flagged, especially if you’re not a person who buys a lot of bling. The reason: Over years of correlating variables, testing, and learning, the system has noticed that a criminal’s first stop after stealing a credit card is often a gas station. If that transaction goes through, the thief knows the card hasn’t yet been reported as stolen and heads off on a spending spree—often at some high-priced retailer.

—J.S.

The Benz is doing most of the driving, keeping us a comfortable distance from the cars ahead with its next-gen cruise-control system. The core of the setup is a pair of radar emitters—a narrow-banded one that pings vehicles up ahead and a wide-angle unit that watches the rest of the traffic and keeps a sharp eye out for jackasses weaving into our lane. All that locational info is fed to the car’s vehicle control unit, a computer that smoothly modulates the brakes and throttle to keep us moving with traffic. The driver specifies a maximum speed, and the car does its best to hit that number—without hitting anything else.

The first time you let the car do its thing is a magically scary experience: You see the cars ahead closing at a rate that activates the “I’m going too fast” reflex; your foot hovers over the brake pedal as your frontal cortex strenuously attempts to override your survival instinct. Cognitively, you know that this system has been meticulously tested by obsessive German engineers who would never let an unsafe car cross the threshold of their shiny factory.

And then, just as you’re contemplating the various safety regulations the car must have complied with on its way to the dealership, you feel yourself slowing—gently, autonomously, in perfect control. The cold cannonball in your gut turns back into warm muscle, and you chuckle softly to yourself for being so silly as to doubt such a well-engineered system. Getting used to these autonomous systems takes time. It turns out that we have to adapt to the machines more than they have to adapt to us.

Cruise control is just the most obvious sign of a particular kind of AI that has been accelerating for decades. Think about it: Antilock brakes know when to back off the pedal. Airbags know that you just smacked into something. Stability control knows that you just overcooked your Volvo into that hairpin and need a little help to stay out of the ditch. Your nav system knows where you are, your wipers know it’s raining, that annoying seat-belt chime knows you’re flouting the law. In short, modern cars are loaded with sensors and computing power. The 2011 Chevy Volt, for example, runs on some 10 million lines of code—more than Lockheed Martin’s new F-35 Joint Strike Fighter.

The marquee innovation that made intelligent cruise control possible is the drive-by-wire throttle: the introduction of motor skills to the automotive body. The throttle is a flap that lets air and fuel enter the engine. In the conventional setup, it’s linked to the gas pedal by a thin metal cable threaded through a grooved wheel. But many newer cars have done away with the cable. Instead, there is a sensor on the gas pedal and a small electric motor on the throttle. Step on the accelerator and an electrical impulse travels to the computer, telling it how far the pedal is depressed; the computer then tells that little electric motor how wide to open the flap. Electronics and software are mediating the whole process. Voilà You’re driving by wire.

Of course, by-wire technology isn’t just for throttles. The same exquisitely sensitive actuation systems are finding their way into brakes and steering as well. And where there are electronically controlled systems, there are sensors and software and processors that can command them. In other words, by-wire technology is paving the way to truly smart cars.

Drive-by-wire didn’t start in the automotive industry. It’s a descendant of an aerospace technology called, yes, fly-by-wire. The first aircraft to fly with it—a Canadian fighter jet called the Avro Canada CF-105 Arrow—took off in 1958. Most of the pilot’s controls, from the elevators to the rudders, were triggered electronically.

The advantages—instantaneous response and lighter weight—were compelling: Within a few decades, many commercial airliners were using fly-by-wire technology. It made every aircraft from the Concorde to the Boeing 777 possible and was integral to improving autopilot systems—including those that can land a plane. It’s nice to have Captain Sullenberger on board, but he’s only needed on special occasions.

The by-wire throttle first made its way into cars in 1988, in the BMW 750iL, and it now makes radar-assisted cruise control possible in any number of Fords, Lincolns, Volvos, Jaguars, and Mercedes. Some hybrids rely on it to switch nimbly between gas and electric power.

The Evolution of

Drive-by-Wire

The autonomous car that whisks you to work while you do sudoku is probably still a couple of decades off, but we’ll get there, thanks to drive-by-wire technology—electronically controlled moving parts that actuate essential components like throttles, steering, and brakes. Here’s a quick history. —Angela Watercutter

1958

The Avro Canada CF-105 Arrow, a supersonic jet built for the Royal Canadian Air Force, makes its debut flight, assisted by the first fly-by-wire controls.

1972

NASA tests a modified F-8 jet with digital electronic controls—and no mechanical backup. It’s the forerunner of systems used in space shuttles.

1988

The Airbus A320 is the first subsonic jetliner to use by-wire tech and pioneers the “glass cockpit,” in which electronic displays replace mechanical ones.

1988

The BMW 750iL is the first production car to use a drive-by-wire throttle. It enables the traction-control system to adjust engine speed and limit wheel spin.

Google tests its own mini-fleet of self-driving Priuses on city streets. Created by Darpa Challenge alums, the cars have already logged more than 140,000 miles.

Illustrations: Brown Bird Design

But drive-by-wire technology has applications beyond the carpool lane that conjure scenes from a sci-fi future: self-driving vehicles that promise the end of traffic jams and a serious reduction in battlefield casualties.

In 2004, Darpa, the Defense Department’s research arm, challenged the big brains of the world to come up with a car that could navigate a complicated desert course with no human input. Employing technologies closely related to our smart cruise control—electronic eyes, computer brains, and drive-by-wire legs—15 teams vied for the million-dollar prize. None finished. But that didn’t stop Darpa from throwing down the gauntlet again. It hosted another challenge the following year, and five of the 23 teams finished. Moore’s law hits the road.

Internet Search

Google’s eyes are everywhere.

A human brain gets visual information from two eyes. Google’s artificial intelligence gets it from billions—through the camera lenses of smartphones. The company collects billions of images from users of Google Goggles, a mobile service that lets you run web searches by taking pictures. Snap a barcode and Goggles will shop for the item’s best price. Take a picture of a book and it’ll link you to, say, a Wikipedia page about the author. Photograph the Eiffel Tower and it’ll give you historical background on the landmark.

At the core of the service is Google’s Superroot Server, software that coordinates the efforts of multiple object-specific recognition engines, each with its own specialized database. There’s one for text, one for landmarks, one for corporate logos, and so on. When an image arrives, Superroot sends it to each of these backend engines, which in turn use a variety of visual-recognition techniques to identify potential matches and compute confidence scores. Superroot then applies its own algorithm to decide which results, if any, to report back to the user.

Because of its modular design, Goggles can easily be expanded to recognize practically anything—and indeed, Google is quickly adding new categories. Next up: identifying plants.

—J.S.

Some 130 miles and nearly seven hours after the second Grand Challenge began, the first car across the finish line was a self-driving Volkswagen Touareg named Stanley—one of the smartest cars ever built. Sebastian Thrun headed up the Stanford team that trained Stanley for its victory and ran head-on into the primary obstacle facing any self-driving car. “You literally can’t even count the number of different situations a driver encounters,” Thrun says.

That’s why his team didn’t try to code a solution for every situation. They taught Stanley how to drive the old-fashioned way: “We took the car out on the road and logged every time it made a mistake.” Stanley’s sensors captured every second of its practice runs. Back at the lab, Thrun’s team used this data to replay failures and challenges over and over again in the car’s software mind as the it simulated different solutions to each puzzle. Every time it failed or succeeded, it learned why.

Thrun has since taken a post at Google, where he and a team of engineers are testing a small fleet of autonomous Toyota Priuses—spawn of Stanley—on the streets and highways of the densely populated San Francisco Bay Area. (Someone sits behind the wheel of the Google cars, ready to take control if necessary.) Of course, you can’t just go out and purchase a robo-vehicle today. Hell, you’re probably still apprehensive about radar-assisted cruise control.

It turns out that the federal agency charged with ensuring auto safety—the National Highway Traffic Safety Administration—shares that fear. NHTSA isn’t going to green-light self-driving cars without a lot more trials and oversight. “It’s not at the point of being sufficiently reliable for the consumer market,” says NHTSA spokesperson Eric Bolton.

Still, the autonomous systems migrating into vehicles are impressively robust and reliable—they make far fewer errors than humans. Plus, there’s no convincing evidence that people will let down their guard when a robot is doing the driving, a phenomenon known as risk compensation. “Do they engage in risky behaviors—texting, applying makeup, shaving?” asks Jim Sayer, who investigates real-world driver behavior at the University of Michigan Transportation Research Institute. “We never see that.”

The real problem arises when millions of humans are confronted with autonomous systems—and some of them freak out. That seems to be what happened recently with some Toyota cars: In a number of well-publicized cases, drivers thought the electronic throttle was improperly accelerating. It turns out that most of the incidents were caused by an all-too-mechanical flaw in the floormat or gas pedal design—or by driver error.

Avoiding those errors is a tricky dance that takes time to learn. Consider the new self-parking technology, brought to the US market by Lexus and since adopted by other carmakers. On a busy city street, I pull a Lincoln MKT (borrowed, again) alongside an empty space and hit a button labeled auto |p|. A two-line LCD on the instrument cluster explains what to do: “Select reverse and take your hands off the wheel.” I follow its commands, and that cannonball forms in my stomach again as the car takes control, whipping the wheel around and backing into the space faster than I would ever attempt. I tell myself to relax, to let go, that this SUV has more sensors than a satellite—a beeping proximity sensor in the back, a rear-facing camera, radar sensors that inform its own magic cruise control. And just as I surrender myself to the future, the Lincoln slams into the car behind me.

A rep from Lincoln later tells me that you’re supposed to work the brake as the car steers itself. And yeah, that two-line display never suggested I take my foot off the pedal; I guess I just assumed that “auto park” meant, you know, auto park. This machine-man language barrier is something we’re really going to have to work on.